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检索条件"任意字段=2024 International Symposium on Computer Architecture and High Performance Computing Workshops"

共 5225 条记录，以下是251-260 订阅

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Random Adaptive Cache Placement Policy 31

Random Adaptive Cache Placement Policy

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31st IEEE international Conference on high performance computing, Data, and Analytics workshops, HiPCW 2024

作者： Ahire, Vrushank Menon, Pranav Muley, Aniruddh Prasad, Abhinandan S. Department of Computer Science and Engineering IIT Ropar Punjab India Department of Mathematics IIT Ropar Punjab India

ISBN: (纸本)9798331509118

This paper presents a new hybrid cache replacement algorithm that combines random allocation with a modified V-Way cache implementation. Our RAC adapts to complex cache access patterns and optimizes cache usage by improving the utilization of cache sets, unlike traditional cache policies. The algorithm utilizes a 16-way set-associative cache with 2048 sets, incorporating dynamic allocation and flexible tag management. RAC extends the V-Way cache design and its variants by optimizing tag and data storage for enhanced efficiency. We evaluated the algorithm using the ChampSim simulator with four diverse benchmark traces and observed significant improvements in cache hit rates up to 80.82% hit rate. Although the improvements in the instructions per cycle (IPC) were moderate, our findings emphasize the algorithm’s potential to enhance cache utilization and reduce memory access times. © 2024 IEEE

关键词： Cache memory

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GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerators 31

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerato...

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31st IEEE international symposium on high performance computer architecture, HPCA 2025

作者： Yang, Siling He, Shuibing Wang, Wenjiong Yin, Yanlong Wu, Tong Chen, Weijian Zhang, Xuechen Sun, Xian-He Feng, Dan The State Key Laboratory of Blockchain and Data Security Zhejiang University China Zhejiang Lab China Institute of Blockchain and Data Security China Zhejiang Key Laboratory of Big Data Intelligent Computing China Washington State University Vancouver United States Illinois Institute of Technology United States Huazhong University of Science and Technology China Wuhan National Laboratory for Optoelectronics China

ISBN: (纸本)9798331506476

Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capability. However, existing accelerators can be severely underutilized even with pipelines, due to the oversight of the skewed execution times of various GCN stages and the ignorance of skewed degrees of graph vertices. In this work, we propose GOPIM, a GCN-oriented pipeline optimization for PIM accelerators to expedite GCN training. First, GOPIM proposes an ML-based scheme that allocates crossbar resources to the most needed stages to streamline the overall pipeline. Second, GOPIM utilizes a selective vertex updating technique that evenly distributes vertices on crossbars by interleaved mapping. These techniques collectively reduce the overall execution time without losing much accuracy. We also provide a practical architecture design for GOPIM. Our experimental results show that, GoPIM achieves up to 191 × speedup and 16.1 × energy saving, compared to the state-of-the-art work. © 2025 IEEE.

关键词： Graph neural networks

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AI Surrogate Model for Distributed computing Workloads

AI Surrogate Model for Distributed Computing Workloads

引用

2024 workshops of the international Conference for high performance computing, Networking, Storage and Analysis, SC workshops 2024

作者： Park, David K. Ren, Yihui Kilic, Ozgur O. Korchuganova, Tatiana Vatsavai, Sairam Sri Boudreau, Joseph Chowdhury, Tasnuva Feng, Shengyu Khan, Raees Kim, Jaehyung Klasky, Scott Maeno, Tadashi Nilsson, Paul Martinez Outschoorn, Verena Ingrid Podhorszki, Norbert Suter, Frederic Yang, Wei Yang, Yiming Yoo, Shinjae Klimentov, Alexei Hoisie, Adolfy Brookhaven National Laboratory UptonNY United States University of Pittsburgh PittsburghPA United States Carnegie Mellon University PittsburghPA United States Oak Ridge National Laboratory Oak RidgeTN United States University of Massachusetts AmherstMA United States SLAC National Accelerator Laboratory Menlo ParkCA United States

ISBN: (纸本)9798350355543

Large-scale international scientific collaborations, such as ATLAS, Belle II, CMS, and DUNE, generate vast volumes of data. These experiments necessitate substantial computational power for varied tasks, including structured data processing, Monte Carlo simulations, and end-user analysis. Centralized workflow and data management systems are employed to handle these demands, but current decision-making processes for data placement and payload allocation are often heuristic and disjointed. This optimization challenge potentially could be addressed using contemporary machine learning methods, such as reinforcement learning, which, in turn, require access to extensive data and an interactive environment. Instead, we propose a generative surrogate modeling approach to address the lack of training data and concerns about privacy preservation. We have collected and processed real-world job submission records, totaling more than two million jobs through 150 days, and applied four generative models for tabular data - TVAE, CTAGGAN+, SMOTE, and TabDDPM - to these datasets, thoroughly evaluating their performance. Along with measuring the discrepancy among feature-wise distributions separately, we also evaluate pair-wise feature correlations, distance to closest record, and responses to pre-trained models. Our experiments indicate that SMOTE and TabDDPM can generate similar tabular data, almost indistinguishable from the ground truth. Yet, as a non-learning method, SMOTE ranks the lowest in privacy preservation. As a result, we conclude that the probabilistic-diffusion-model-based TabDDPM is the most suitable generative model for managing job record data. © 2024 IEEE.

关键词： AI-based performance Modeling Distributed Workflows high-performance computing Simulation Surrogate Models

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DAPPER: A performance-Attack-Resilient Tracker for RowHammer Defense 31

DAPPER: A Performance-Attack-Resilient Tracker for RowHammer...

引用

31st IEEE international symposium on high performance computer architecture, HPCA 2025

作者： Woo, Jeonghyun Nair, Prashant J. The University of British Columbia Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798331506476

RowHammer vulnerabilities pose a significant threat to modern DRAM-based systems, where rapid activation of DRAM rows can induce bit-flips in neighboring rows. To mitigate this, state-of-the-art host-side RowHammer mitigations typically rely on shared counters or tracking structures. While these optimizations benefit benign applications, they are vulnerable to performance Attacks (Perf-Attacks), where adversaries exploit shared structures to reduce DRAM bandwidth for co-running benign applications by increasing DRAM accesses for RowHammer counters or triggering repetitive refreshes required for the early reset of structures, significantly degrading performance. In this paper, we propose secure hashing mechanisms to thwart adversarial attempts to capture the mapping of shared structures. We propose DAPPER, a novel low-cost tracker resilient to Perf-Attacks even at ultra-low RowHammer thresholds. We first present a secure hashing template in the form of DAPPER-S. We then develop Dapper-H, an enhanced version of DapperS, incorporating double-hashing, novel reset strategies, and mitigative refresh techniques. Our security analysis demonstrates the effectiveness of DAPPER-H against both RowHammer and Perf-Attacks. Experiments with 57 workloads from SPEC2006, SPEC2017, TPC, Hadoop, MediaBench, and YCSB show that, even at an ultra-low RowHammer threshold of 500, DapperH incurs only a 0.9% slowdown in the presence of Perf-Attacks while using only 96 KB of SRAM per 32GB of DRAM memory. © 2025 IEEE.

关键词： Static random access storage

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A Metric for HPC Programming Model Productivity

A Metric for HPC Programming Model Productivity

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2024 workshops of the international Conference for high performance computing, Networking, Storage and Analysis, SC workshops 2024

作者： Lin, Wei-Chen Deakin, Tom Mcintosh-Smith, Simon University of Bristol School of Computer Science Bristol United Kingdom

ISBN: (纸本)9798350355543

There has been a healthy growth of heterogeneous programming models that cover different paradigms in the HPC space. Selecting an appropriate programming model for new projects is challenging: how does one select a model that is both productive and performant? The same applies for existing projects aiming to leverage heterogeneous offload *** characterisation of programming model performance has been abundant and comprehensive, productivity metrics are often reduced to basic measures like Source Line of Code (SLOC). This study introduces a novel model divergence measure to objectively evaluate productivity. We cover common aspects of productivity, including syntax, semantics, and optimisation *** present a productivity analysis framework supporting GCC and Clang, covering models for C/C++ and Fortran. We evaluate our metric using this framework on select miniapps, including some from SPEChpc, and propose a combined productivity and performance probability visualisation for a comprehensive picture. © 2024 IEEE.

关键词： benchmarking CUDA Fortran HIP Kokkos OpenMP OpenMP target performance portability productivity semantics StdPar SYCL TBB

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AMMA: Adaptive Multimodal Assistants Through Automated State Tracking and User Model-Directed Guidance Planning 31

AMMA: Adaptive Multimodal Assistants Through Automated State...

引用

IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

作者： Yang, Jackie (Junrui) Qiu, Leping Corona-Moreno, Emmanuel Angel Shi, Louisa Bui, Hung Lam, Monica S. Landay, James A. Stanford Univ Stanford CA 94305 USA Univ Toronto Toronto ON Canada Univ Washington Seattle WA USA

ISBN: (纸本)9798350374025;9798350374032

Novel technologies such as augmented reality and computer perception lay the foundation for smart assistants that can guide us through real-world tasks, such as cooking or home repair. However, the nature of real-world interaction requires assistants that adapt to users' mistakes, environments, and communication preferences. We propose Adaptive Multimodal Assistants (AMMA), a software architecture for task guidance with generated adaptive interfaces from step-by-step instructions. This is achieved through 1) an automatically generated user action state tracker and 2) a guidance planner that leverages a continuously trained user model. The assistant also adjusts its guidance and communication delivery methods based on observed user performance as well as implicit and explicit user feedback. We demonstrated the viability of AMMA by building an adaptive cooking assistant running in a high-fidelity virtual reality-based simulator. A user study of the cooking assistant showed that AMMA can reduce the task completion time and the number of manual communication methods changes.

关键词： Augmented reality interface generation smart assistant Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality Human-centered computing-Human computer interaction (HCI)-Interactive systems and tools-User interface toolkits

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FxHENN: FPGA-based acceleration framework for homomorphic encrypted CNN inference 29

FxHENN: FPGA-based acceleration framework for homomorphic en...

引用

29th IEEE international symposium on high-performance computer architecture (HPCA)

作者： Zhu, Yilan Wang, Xinyao Ju, Lei Guo, Shanqing Shandong Univ Key Lab Cryptol Technol & Informat Secur Minist Educ Qingdao Shandong Peoples R China Shandong Univ Sch Cyber Sci & Technol Qingdao Peoples R China Quancheng Lab Jinan Peoples R China

ISBN: (纸本)9781665476522

Fully homomorphic encryption (FHE) is a promising data privacy solution for machine learning, which allows the inference to be performed with encrypted data. However, it typically leads to 5-6 orders of magnitude higher computation and storage overhead. This paper proposes the first full-fledged FPGA acceleration framework for FHE-based convolution neural network (HE-CNN) inference. We then design parameterized HE operation modules with intra- and inter- HE-CNN layer resource management based on FPGA high-level synthesis (HLS) design flow. With sophisticated resource and performance modeling of the HE operation modules, the proposed FxHENN framework automatically performs design space exploration to determine the optimized resource provisioning and generates the accelerator circuit for a given HE-CNN model on a target FPGA device. Compared with the state-of-the-art CPU-based HE-CNN inference solution, FxHENN achieves up to 13.49X speedup of inference latency, and 1187.12X energy efficiency. Meanwhile, given this is the first attempt in the literature on FPGA acceleration of fullfledged non-interactive HE-CNN inference, our results obtained on low-power FPGA devices demonstrate HE-CNN inference for edge and embedded computing is practical.

关键词： fully homomorphic encryption convolution neural network FPGA acceleration high-level synthesis design space exploration

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A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

A Scalable Real-Time Data Assimilation Framework for Predict...

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2024 workshops of the international Conference for high performance computing, Networking, Storage and Analysis, SC workshops 2024

作者： Yin, Junqi Liang, Siming Liu, Siyan Bao, Feng Chipilski, Hristo G. Lu, Dan Zhang, Guannan Oak Ridge National Laboratory National Center for Computational Science Oak RidgeTN United States Florida State University Department of Mathematics TallahasseeFL United States Oak Ridge National Laboratory Computational Science and Engineering Division Oak RidgeTN United States Florida State University Department of Scientific Computing TallahasseeFL United States Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States

ISBN: (纸本)9798350355543

AI-based foundation models like FourCastNet, GraphCast, ClimaX, and Pangu-Weather are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones and atmospheric rivers. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, e.g., the Local Ensemble Transform Kalman Filter (LETKF);and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions. Even though the proposed framework is tested only on a benchmark surface quasi-geostrophic (SQG) turbulence system, it has the potential to be combined with existing AI-based foundation models, making it suitable for future operational implementations. © 2024 IEEE.

关键词： data assimilation Diffusion models foundation models generative AI high-performance computing uncertainty quantification

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Configuration of Neural Network Hyperparameter using Ant Colony Optimization Algorithm 31

Configuration of Neural Network Hyperparameter using Ant Col...

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31st IEEE international Conference on high performance computing, Data, and Analytics workshops, HiPCW 2024

作者： Singh, Anuradha Kumari Karthikeyan, S. Department of Computer Science Banaras Hindu University Varanasi India

ISBN: (纸本)9798331509118

Deep neural networks are widely used in classification and prediction tasks. These problems are typically solved using multilayer neural networks. However, one of the most challenging tasks is setting the hyperparameters of these networks. This process is time-consuming because hyperparameters are still often chosen through a trial-and-error approach. In this paper, we propose a metaheuristic optimization technique to automatically set the hyperparameters of a neural network for prediction tasks. Specifically, we introduce a new ant colony optimization algorithm (ACO-NN) to configure the number of neurons in the hidden layers of the network. The proposed approach is validated using data from undergraduate students, which includes demographic, socioeconomic, microeconomic information, data at enrollment, and academic records from their first and second semesters. We evaluate the performance of our method on this dataset and compare our results with those of other machine learning algorithms. In all cases, our approach achieves better results in terms of test accuracy, precision, recall, and F1-score. © 2024 IEEE

关键词： Ant colony optimization

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Entanglement Capacity Estimates and Throughput Measurements of Quantum Channels

Entanglement Capacity Estimates and Throughput Measurements ...

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2024 workshops of the international Conference for high performance computing, Networking, Storage and Analysis, SC workshops 2024

作者： Rao, Nageswara S. V. Alshowkan, Muneer Chapman, Joseph C. Peters, Nicholas A. Lu, Hsuan-Hao Lukens, Joseph M. Guha, Saikat Oak Ridge National Laboratory Computational Sciences and Engineering Division Oak Ridge United States Arizona State University Research Technology Office TempeAZ United States University of Maryland Department of Electrical and Computer Engineering College ParkMD United States

ISBN: (纸本)9798350355543

The throughput is an important performance metric of entangled qubit distribution quantum networks, and may be characterized by the number of distributed entangled qubit pairs per second (ebps). It is measured over physical quantum network connections using specialized instruments, including photonic entanglement sources and single photon detectors. Extensive theory has been developed to estimate the entangled qubit capacity of quantum channels using abstractions of physical connections. These two quantities both characterize the throughput performance but in different ways, and typically have been hard to relate to each other in concrete terms, in part due to the lack of precise measurements with matching analytical models and derivations. We describe measurements on a physical testbed with fiber connections of lengths 0-75 kilometers. We obtain the normalized analytic capacity estimates using the transmissivity approximations derived using single photon coincidence measurements, and convert them to bounds on throughput (measured in ebps) using a multiplier derived from co-located detector measurements. The results indicate consistent throughput measurements upper-bounded by their analytical capacity estimates across all connections. We show that previous capacity estimates using light measurements are below ebps measurements for some connections, due to the inclusion of non-representative decrease of light levels outside C-band with distance. © 2024 IEEE.

关键词： channel capacity ebit entangled bits per second quantum network testbed throughput estimates

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